Timing is a critical element of most behaviors and of many cognitive tasks, yet we have little understanding of how neural circuits estimate, remember and control time intervals or generate temporal sequences of activity. A new class of network models will be studied that show great promise for uncovering the dynamic mechanisms, operating at the neural circuit level, supporting sequence generation and timing computations. These models will be built from a generic network structure through the addition of tuned feedback loops. This allows the same network to perform many different tasks and resembles the reconfiguration of existing circuits that occurs when a new task is learned. Constructing realistic models that perform complex tasks raises the possibility of making deep connections between the dynamic mechanisms operating in model and real neuronal networks. Three steps are required to fulfill this promise, and these are the three specific aims of the proposal. First, a study will be undertaken to determine what motor and cognitive tasks network models are capable of performing and to relate their level of performance to that of animals and humans performance analogous tasks. Second, the models will be made realistic enough to make definitive and predicted statements about experimental data. Third, an approach will be developed so that the network models can function as a tool to uncover the dynamic mechanisms operating in real neural circuits. For this purpose a collaboration has been established with experimental colleagues acquiring multi-electrode recordings from monkeys performing delayed-reaching tasks. Successfully accomplishing these three aims will significantly advance understanding of how timing arises from neural circuit dynamics and lead to hypotheses about circuit malfunctions that cause defects in timing estimation and movement initiation and control.